code: def load_bace_regression(
      featurizer: Union[dc.feat.Featurizer, str] = "ECFP",
      splitter: Union[dc.splits.Splitter, str, None] = "scaffold",
      transformers: List[Union[TransformerGenerator, str]] = [
          "normalization"
      ],
      reload: bool = True,
      data_dir: Optional[str] = None,
      save_dir: Optional[str] = None,
      **kwargs,
  ) -> Tuple[
      List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]
  ]:
      """Load BACE dataset, regression labels
  
      The BACE dataset provides quantitative IC50 and qualitative (binary label)
      binding results for a set of inhibitors of human beta-secretase 1 (BACE-1).
  
      All data are experimental values reported in scientific literature over the
      past decade, some with detailed crystal structures available. A collection
      of 1522 compounds is provided, along with the regression labels of IC50.
  
      Scaffold splitting is recommended for this dataset.
  
      The raw data csv file contains columns below:
  
      - "mol" - SMILES representation of the molecular structure
      - "pIC50" - Negative log of the IC50 binding affinity
      - "class" - Binary labels for inhibitor
  
      Parameters
      ----------
      featurizer: Featurizer or str
          the featurizer to use for processing the data.  Alternatively you can pass
          one of the names from dc.molnet.featurizers as a shortcut.
      splitter: Splitter or str
          the splitter to use for splitting the data into training, validation, and
          test sets.  Alternatively you can pass one of the names from
          dc.molnet.splitters as a shortcut.  If this is None, all the data
          will be included in a single dataset.
      transformers: list of TransformerGenerators or strings
          the Transformers to apply to the data.  Each one is specified by a
          TransformerGenerator or, as a shortcut, one of the names from
          dc.molnet.transformers.
      reload: bool
          if True, the first call for a particular featurizer and splitter will cache
          the datasets to disk, and subsequent calls will reload the cached datasets.
      data_dir: str
          a directory to save the raw data in
      save_dir: str
          a directory to save the dataset in
  
      References
      ----------
      .. [1] Subramanian, Govindan, et al. "Computational modeling of β-secretase 1
          (BACE-1) inhibitors using ligand based approaches." Journal of chemical
          information and modeling 56.10 (2016): 1936-1949.
      """
      loader = _BaceLoader(
          featurizer,
          splitter,
          transformers,
          BACE_REGRESSION_TASKS,
          data_dir,
          save_dir,
          **kwargs,
      )
      return loader.load_dataset("bace_r", reload)
  
file_path: deepchem/deepchem/molnet/load_function/bace_datasets.py
imports: import os
  import deepchem
  from deepchem.molnet.load_function.molnet_loader import TransformerGenerator, _MolnetLoader
  from deepchem.data import Dataset
  from typing import List, Optional, Tuple, Union
label_desc: Use image node_id 1 for calling a global function with example usage: load_bace_regression(featurizer, splitter, transformers, reload, data_dir, save_dir) and returns: loader